scholarly journals Switching On Static Gene Regulatory Networks to Compute Cellular Decisions

2020 ◽  
Author(s):  
Clara E. Pavillet ◽  
Dimitrios Voukantsis ◽  
Francesca M. Buffa
Keyword(s):  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Computational Models ◽  
Cell Behaviour ◽  
Cellular Processes ◽  
Health And Disease ◽  
Gene Regulatory ◽  
Cellular Behaviour

AbstractMotivationGene networks are complex sets of regulators and interactions that govern cellular processes. Their perturbations can disrupt regular biological functions, translating into a change in cell behaviour and ability to respond to internal and external cues. Computational models of these networks can boost translation of our scientific knowledge into medical applications by predicting how cells will behave in health and disease, or respond to stimuli such as a drug treatment. The development of such models requires effective ways to read, manipulate and analyse the increasing amount of existing, and newly deposited gene network data.ResultsWe developed BioSWITCH, a command-line program using the BioPAX standardised language to “switch on” static regulatory networks so that they can be executed in GINML to predict cellular behaviour. Using a previously published haematopoiesis gene network, we show that BioSWITCH successfully and faithfully automates the network de-coding and re-coding into an executable logical network. BioSWITCH also supports the integration of a BioPAX model into an existing GINML graph.AvailabilitySource code available at https://github.com/CBigOxf/[email protected]; [email protected]

A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks

2017 ◽  
Vol 15 (02) ◽  
pp. 1650045 ◽  
Author(s):  
Olga V. Petrovskaya ◽  
Evgeny D. Petrovskiy ◽  
Inna N. Lavrik ◽  
Vladimir A. Ivanisenko
Keyword(s):  
Mathematical Modeling ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Network Modeling ◽  
Computer Experiments ◽  
Linear Control ◽  
Expression Of Genes ◽  
Gene Regulatory

Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

scholarly journals Universal attenuators and their interactions with feedback loops in gene regulatory networks

2016 ◽  
Author(s):  
Dianbo Liu ◽  
Luca Albergante ◽  
Timothy J Newman
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Human Cancer ◽  
Cancer Cell Line ◽  
Feedback Loops ◽  
Human Cancer Cell ◽  
E Coli ◽  
Gene Regulatory

AbstractUsing a combination of mathematical modelling, statistical simulation and large-scale data analysis we study the properties of linear regulatory chains (LRCs) within gene regulatory networks (GRNs). Our modelling indicates that downstream genes embedded within LRCs are highly insulated from the variation in expression of upstream genes, and thus LRCs act as attenuators. This observation implies a progressively weaker functionality of LRCs as their length increases. When analysing the preponderance of LRCs in the GRNs of E. coli K12 and several other organisms, we find that very long LRCs are essentially absent. In both E. coli and M. tuberculosis we find that four-gene LRCs are intimately linked to identical feedback loops that are involved in potentially chaotic stress response, indicating that the dynamics of these potentially destabilising motifs are strongly restrained under homeostatic conditions. The same relationship is observed in a human cancer cell line (K562), and we postulate that four-gene LRCs act as “universal attenuators”. These findings suggest a role for long LRCs in dampening variation in gene expression, thereby protecting cell identity, and in controlling dramatic shifts in cell-wide gene expression through inhibiting chaos-generating motifs.In briefWe present a general principle that linear regulatory chains exponentially attenuate the range of expression in gene regulatory networks. The discovery of a universal interplay between linear regulatory chains and genetic feedback loops in microorganisms and a human cancer cell line is analysed and discussed.HighlightsWithin gene networks, linear regulatory chains act as exponentially strong attenuators of upstream variationBecause of their exponential behaviour, linear regulatory chains beyond a few genes provide no additional functionality and are rarely observed in gene networks across a range of different organismsNovel interactions between four-gene linear regulatory chains and feedback loops were discovered in E. coli, M. tuberculosis and human cancer cells, suggesting a universal mechanism of control.

scholarly journals CoGNaC: A Chaste Plugin for the Multiscale Simulation of Gene Regulatory Networks Driving the Spatial Dynamics of Tissues and Cancer

2015 ◽  
Vol 14s4 ◽  
pp. CIN.S19965 ◽  
Author(s):  
Simone Rubinacci ◽  
Alex Graudenzi ◽  
Giulio Caravagna ◽  
Giancarlo Mauri ◽  
James Osborne ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Dynamical Behavior ◽  
Gene Activation ◽  
Spatial Dynamics ◽  
Multiscale Simulation ◽  
Activation Patterns ◽  
Gene Regulatory ◽  
Cellular Phenotypes

We introduce a Chaste plugin for the generation and the simulation of Gene Regulatory Networks (GRNs) in multiscale models of multicellular systems. Chaste is a widely used and versatile computational framework for the multiscale modeling and simulation of multicellular biological systems. The plugin, named CoGNaC (Chaste and Gene Networks for Cancer), allows the linking of the regulatory dynamics to key properties of the cell cycle and of the differentiation process in populations of cells, which can subsequently be modeled using different spatial modeling scenarios. The approach of CoGNaC focuses on the emergent dynamical behavior of gene networks, in terms of gene activation patterns characterizing the different cellular phenotypes of real cells and, especially, on the overall robustness to perturbations and biological noise. The integration of this approach within Chaste's modular simulation framework provides a powerful tool to model multicellular systems, possibly allowing for the formulation of novel hypotheses on gene regulation, cell differentiation, and, in particular, cancer emergence and development. In order to demonstrate the usefulness of CoGNaC over a range of modeling paradigms, two example applications are presented. The first of these concerns the characterization of the gene activation patterns of human T-helper cells. The second example is a multiscale simulation of a simplified intestinal crypt, in which, given certain conditions, tumor cells can emerge and colonize the tissue.

scholarly journals AGENT: the Arabidopsis Gene Regulatory Network Tool for Exploring and Analyzing GRNs

2021 ◽  
Author(s):  
Vincent Lau ◽  
Rachel Woo ◽  
Bruno Pereira ◽  
Asher Pasha ◽  
Eddi Esteban ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Ad Hoc ◽  
Arabidopsis Gene ◽  
Nitrate Signaling ◽  
Multi Level ◽  
Gene Regulatory

AbstractGene regulatory networks (GRNs) are complex networks that capture multi-level regulatory events between one or more regulatory macromolecules, such as transcription factors (TFs), and their target genes. Advancements in screening technologies such as enhanced yeast-one-hybrid screens have allowed for high throughput determination of GRNs. However, visualization of GRNs in Arabidopsis has been limited to ad hoc networks and are not interactive. Here, we describe the Arabidopsis GEne Network Tool (AGENT) that houses curated GRNs and provides tools to visualize and explore them. AGENT features include expression overlays, subnetwork motif scanning, and network analysis. We show how to use AGENT’s multiple built-in tools to identify key genes that are involved in flowering and seed development along with identifying temporal multi-TF control of a key transporter in nitrate signaling. AGENT can be accessed at https://bar.utoronto.ca/AGENT.

scholarly journals Assigning function to natural allelic variation via dynamic modeling of gene network induction

2017 ◽  
Author(s):  
Magali Richard ◽  
Florent Chuffart ◽  
Hélène Duplus-Bottin ◽  
Fanny Pouyet ◽  
Martin Spichty ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Computational Models ◽  
Allelic Variation ◽  
Model Parameters ◽  
Or Efficiency ◽  
Response Network ◽  
Dna Variants ◽  
Gene Regulatory ◽  
Regulatory Models

ABSTRACTMore and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be ‘personalized’ according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non-synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants.

scholarly journals Neural Gene Network Constructor: A Neural Based Model for Reconstructing Gene Regulatory Network

2019 ◽  
Author(s):  
Zhang Zhang ◽  
Lifei Wang ◽  
Shuo Wang ◽  
Ruyi Tao ◽  
Jingshu Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Regulatory

SummaryReconstructing gene regulatory networks (GRNs) and inferring the gene dynamics are important to understand the behavior and the fate of the normal and abnormal cells. Gene regulatory networks could be reconstructed by experimental methods or from gene expression data. Recent advances in Single Cell RNA sequencing technology and the computational method to reconstruct trajectory have generated huge scRNA-seq data tagged with additional time labels. Here, we present a deep learning model “Neural Gene Network Constructor” (NGNC), for inferring gene regulatory network and reconstructing the gene dynamics simultaneously from time series gene expression data. NGNC is a model-free heterogenous model, which can reconstruct any network structure and non-linear dynamics. It consists of two parts: a network generator which incorporating gumbel softmax technique to generate candidate network structure, and a dynamics learner which adopting multiple feedforward neural networks to predict the dynamics. We compare our model with other well-known frameworks on the data set generated by GeneNetWeaver, and achieve the state of the arts results both on network reconstruction and dynamics learning.

scholarly journals The Contribution of Autophagy and LncRNAs to MYC-Driven Gene Regulatory Networks in Cancers

2021 ◽  
Vol 22 (16) ◽  
pp. 8527
Author(s):  
Leila Jahangiri ◽  
Perla Pucci ◽  
Tala Ishola ◽  
Ricky M. Trigg ◽  
John A. Williams ◽  
...  
Keyword(s):  
Gene Networks ◽  
Regulatory Networks ◽  
Metabolic Reprogramming ◽  
Cellular Processes ◽  
Cancer Genomes ◽  
Mutual Regulation ◽  
Myc Gene ◽  
Gene Regulatory ◽  
Non Coding Rnas

MYC is a target of the Wnt signalling pathway and governs numerous cellular and developmental programmes hijacked in cancers. The amplification of MYC is a frequently occurring genetic alteration in cancer genomes, and this transcription factor is implicated in metabolic reprogramming, cell death, and angiogenesis in cancers. In this review, we analyse MYC gene networks in solid cancers. We investigate the interaction of MYC with long non-coding RNAs (lncRNAs). Furthermore, we investigate the role of MYC regulatory networks in inducing changes to cellular processes, including autophagy and mitophagy. Finally, we review the interaction and mutual regulation between MYC and lncRNAs, and autophagic processes and analyse these networks as unexplored areas of targeting and manipulation for therapeutic gain in MYC-driven malignancies.

scholarly journals The Role of Gene Conversion between Transposable Elements in Rewiring Regulatory Networks

2019 ◽  
Vol 11 (7) ◽  
pp. 1723-1729
Author(s):  
Jeffrey A Fawcett ◽  
Hideki Innan
Keyword(s):  
Transposable Elements ◽  
Gene Conversion ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Alternative Mechanism ◽  
Beneficial Mutations ◽  
Future Studies ◽  
Cellular Processes ◽  
Gene Regulatory

Abstract Nature has found many ways to utilize transposable elements (TEs) throughout evolution. Many molecular and cellular processes depend on DNA-binding proteins recognizing hundreds or thousands of similar DNA motifs dispersed throughout the genome that are often provided by TEs. It has been suggested that TEs play an important role in the evolution of such systems, in particular, the rewiring of gene regulatory networks. One mechanism that can further enhance the rewiring of regulatory networks is nonallelic gene conversion between copies of TEs. Here, we will first review evidence for nonallelic gene conversion in TEs. Then, we will illustrate the benefits nonallelic gene conversion provides in rewiring regulatory networks. For instance, nonallelic gene conversion between TE copies offers an alternative mechanism to spread beneficial mutations that improve the network, it allows multiple mutations to be combined and transferred together, and it allows natural selection to work efficiently in spreading beneficial mutations and removing disadvantageous mutations. Future studies examining the role of nonallelic gene conversion in the evolution of TEs should help us to better understand how TEs have contributed to evolution.

Using Bayesian Networks to Construct Gene Regulatory Networks from Microarray Data

2012 ◽  
pp. 1-6
Author(s):  
Ai Kung Tan ◽  
Mohd Saberi Mohamad
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Cell Cycle ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Cell Cycle Gene ◽  
Gene Regulatory ◽  
Cell Cycle Gene Expression

In this research, Bayesian network is proposed as the model to construct gene regulatory networks from Saccharomyces cerevisiae cell-cycle gene expression dataset and Escherichia coli dataset due to its capability of handling microarray datasets with missing values. The goal of this research is to study and to understand the framework of the Bayesian networks, and then to construct gene regulatory networks from Saccharomyces cerevisiae cell-cycle gene expression dataset and Escherichia coli dataset by developing Bayesian networks using hill-climbing algorithm and Efron’s bootstrap approach and then the performance of the constructed gene networks of Saccharomyces cerevisiae are evaluated and are compared with the previously constructed sub-networks by Dejori [14]. At the end of this research, the gene networks constructed for Saccharomyces cerevisiae not only have achieved high True Positive Rate (more than 90%), but the networks constructed also have discovered more potential interactions between genes. Therefore, it can be concluded that the performance of the gene regulatory networks constructed using Bayesian networks in this research is proved to be better because it can reveal more gene relationships. Dalam penyelidikan ini, Bayesian network adalah dicadangkan sebagai model untuk membina gene regulatory networks dari kitar sel S. cerevisiae set data disebabkan keupayaannya untuk mengendali set data microarray yang mempunyai nilai-nilai yang hilang. Tujuan penyelidikan ini adalah untuk mempelajari dan memahami rekabentuk untuk Bayesian network, dan kemudian untuk membina gene regulatory networks dari data Saccharomyces cerevisiae cell-cycle gene expression dan data Escherichia coli dengan membina model Bayesian networks dengan menggunakan algoritma hill-climbing serta Efron’s bootstrap approach dan gene networks yang dibina untuk Saccharomyces cerevisiae dibandingkan dengan sub-networks yang dibina oleh Dejori [14]. Pada akhir kajian ini, gene networks yang dibina untuk Saccharomyces cerevisiae bukan sahaja telah mencapai True Positive Rate yang tinggi (lebih dari 90%), tetapi gene networks yang dibina juga telah menemui lebih banyak interaksi berpotensi antara gen. Oleh kerana itu, dapat disimpulkan bahawa prestasi gene networks yang dibina menggunakan Bayesian network dalam kajian ini adalah terbukti lebih baik kerana ia boleh mendedahkan lebih banyak hubungan antara gen.

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